#Import libraries 
import pandas as pd
import numpy as np
import datetime as df

# Use Pandas to read stock data (the csv file is included in the course package) 
stock_df = pd.read_csv('AMZN.csv')
stock_df.head(15)

# Count the number of missing values in "stock_df" Pandas DataFrame
stock_df.isnull().sum()

Date         0
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

# Obtain information about the Pandas DataFrame such as data types, memory utilization..etc
stock_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 250 entries, 0 to 249
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       250 non-null    object 
 1   Open       250 non-null    float64
 2   High       250 non-null    float64
 3   Low        250 non-null    float64
 4   Close      250 non-null    float64
 5   Adj Close  250 non-null    float64
 6   Volume     250 non-null    int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 13.8+ KB

# Calculate the percentage daily return
stock_df['Daily Return'] = stock_df['Adj Close'].pct_change(1) * 100
stock_df

#Replace the Not a Number, NaN, with 0 instead.
stock_df['Daily Return'].replace(np.nan, 0, inplace = True)
stock_df

# Use the describe() method to obtain a statistical summary about the data 
stock_df.describe().round(2)

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Plot a Line Plot Using Plotly Express, for the adjusted closing price of Amazon
fig = px.line(title = 'Amazon.com, Inc. (AMZN) Adjusted Closing Price [$]')
fig.add_scatter(x = stock_df['Date'], y = stock_df['Adj Close'], name = 'Adj Close')

stock_df

# Define a function that performs interactive data visualization using Plotly Express
def plot_financial_data(df, title):
    
    fig = px.line(title = title)
    
    # For loop that plots all stock prices in the pandas dataframe df
    # Note that index starts with 1 because we want to skip the date column
    
    for i in df.columns[1:]:
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        fig.update_traces(line_width = 5)
        fig.update_layout({'plot_bgcolor': "white"})

    fig.show()

# Plot High, Low, Open, Close and Adj Close
plot_financial_data(stock_df.drop(['Volume', 'Daily Return'], axis = 1), 'Amazon.com, Inc. (AMZN) Stock Price [$]')

# Plot trading volume
plot_financial_data(stock_df.iloc[:,[0,5]], 'Amazon.com, Inc. (AMZN) Trading Volume')

# Plot % Daily Returns
plot_financial_data(stock_df.iloc[:,[0,7]], 'Amazon.com, Inc. (AMZN) Percentage Daily Return [%]')

# Define a function that classifies the returns based on the magnitude
# Feel free to change these numbers
def percentage_return_classifier(percentage_return):
    
    if percentage_return > -0.3 and percentage_return <= 0.3:
        return 'Insignificant Change'
    elif percentage_return > 0.3 and percentage_return <= 3:
        return 'Positive Change'
    elif percentage_return > -3 and percentage_return <= -0.3:
        return 'Negative Change'
    elif percentage_return > 3 and percentage_return <= 7:
        return 'Large Positive Change'
    elif percentage_return > -7 and percentage_return <= -3:
        return 'Large Negative Change'
    elif percentage_return > 7:
        return 'Bull Run'
    elif percentage_return <= -7:
        return 'Bear Sell Off'

# Apply the function to the "Daily Return" Column and place the result in "Trend" column
stock_df['Trend'] = stock_df['Daily Return'].apply(percentage_return_classifier)
stock_df

# Count distinct values in the Trend column
trend_summary = stock_df['Trend'].value_counts()
trend_summary

Trend
Positive Change          98
Negative Change          87
Insignificant Change     48
Large Positive Change     8
Large Negative Change     7
Bull Run                  2
Name: count, dtype: int64

# Plot a pie chart using Matplotlib Library
plt.figure(figsize = (8, 8))
trend_summary.plot(kind = 'pie', y = 'Trend');

# Let's plot a candlestick graph using Cufflinks library
# Cufflinks is a powerful Python library that connects Pandas and Plotly for generating plots using few lines of code
# Cufflinks allows for interactive data visualization
import cufflinks as cf
cf.go_offline() # Enabling offline mode for interactive data visualization locally

stock_df

# Set the date to be the index for the Pandas DataFrame
# This is critical to show the date on the x-axis when using cufflinks
stock_df.set_index(['Date'], inplace = True)
stock_df

# Plot Candlestick figure using Cufflinks QuantFig module
figure = cf.QuantFig(stock_df, title = 'Amazon.com, Inc. (AMZN) Candlestick Chart', name = 'AMZN')
figure.add_sma(periods =[14, 21], column = 'Close', color = ['magenta', 'green'])
figure.iplot(theme = 'white', up_color = 'green', down_color = 'red')

close_price_df = pd.read_csv('stock_data.csv')
close_price_df

# Remove the "Date" column (first column) and the last 10 columns (Volume columns)
adjusted_close_df = close_price_df.iloc[:, 1:-10]

# Calculate the percentage daily return for the remaining columns
daily_returns_df = adjusted_close_df.pct_change() * 100

# Replace NaN values with 0
daily_returns_df.replace(np.nan, 0, inplace=True)

# Display the daily returns DataFrame
daily_returns_df

daily_returns_subset = daily_returns_df.iloc[:, :10]
daily_returns_subset

# Insert the date column at the start of the Pandas DataFrame (@ index = 0)
daily_returns_df.insert(0, "Date", close_price_df['Date'])
daily_returns_df

daily_returns_subset.insert(0, "Date", close_price_df['Date'])
daily_returns_subset

# Plot the stocks daily returns
plot_financial_data(daily_returns_subset, 'Percentage Daily Returns [%]')

# Plot a heatmap showing the correlations between daily returns
# Strong positive correlations between Catterpillar and John Deere - both into heavy equipment and machinery
# META and Google - both into Tech and Cloud Computing
plt.figure(figsize = (10, 8))
sns.heatmap(daily_returns_subset.drop(columns = ['Date']).corr(), annot = True);

# Plot the Pairplot between stocks daily returns
sns.pairplot(daily_returns_subset);

# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[1:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df

price_scaling(close_price_df)

import random

def generate_portfolio_weights(n):
    weights = []
    for i in range(n):
        weights.append(random.random())
        
    # let's make the sum of all weights add up to 1
    weights = weights/np.sum(weights)
    return weights

# Call the function (Run this cell multiple times to generate different outputs)
weights = generate_portfolio_weights(10)
print(weights)

[0.12283562 0.11085941 0.07078284 0.00170883 0.13510652 0.1370366
 0.13327715 0.1258203  0.08218237 0.08039037]

# Let's display "close_price_df" Pandas DataFrame
close_price_df
# Select the "Date" column and the next 10 columns
columns_to_keep = close_price_df.columns[:11]  # First column (Date) + next 10 columns

# Create a new DataFrame with only the selected columns
close_price_df_subset = close_price_df[columns_to_keep]

# Display the resulting DataFrame
close_price_df_subset

# Scale stock prices using the "price_scaling" function that we defined earlier (make all stock values start at 1)
portfolio_df = close_price_df_subset.copy()
scaled_df = price_scaling(portfolio_df)
scaled_df

portfolio_df

# Use enumerate() method to obtain the stock names along with a counter "i" (0, 1, 2, 3,..etc.)
# This counter "i" will be used as an index to access elements in the "weights" list
initial_investment = 1000000
for i, stock in enumerate(scaled_df.columns[1:]):
    portfolio_df[stock] = weights[i] * scaled_df[stock]  * initial_investment
portfolio_df.round(1)

def asset_allocation(df, weights, initial_investment):
    portfolio_df = df.copy()

    # Scale stock prices using the "price_scaling" function that we defined earlier (Make them all start at 1)
    scaled_df = price_scaling(df)
  
    for i, stock in enumerate(scaled_df.columns[1:]):
        portfolio_df[stock] = scaled_df[stock] * weights[i] * initial_investment

    # Sum up all values and place the result in a new column titled "portfolio value [$]" 
    # Note that we excluded the date column from this calculation
    portfolio_df['Portfolio Value [$]'] = portfolio_df[portfolio_df != 'Date'].sum(axis = 1, numeric_only = True)
            
    # Calculate the portfolio percentage daily return and replace NaNs with zeros
    portfolio_df['Portfolio Daily Return [%]'] = portfolio_df['Portfolio Value [$]'].pct_change(1) * 100 
    portfolio_df.replace(np.nan, 0, inplace = True)
    
    return portfolio_df

n = len(close_price_df_subset.columns)-1

# Let's generate random weights 
print('Number of stocks under consideration = {}'.format(n))
weights = generate_portfolio_weights(n).round(6)
print('Portfolio weights = {}'.format(weights))

Number of stocks under consideration = 10
Portfolio weights = [0.086754 0.024816 0.12297  0.085382 0.128072 0.123358 0.165131 0.131114
 0.117482 0.01492 ]

# Let's test out the "asset_allocation" function
portfolio_df = asset_allocation(close_price_df_subset, weights, 1000000)
portfolio_df.round(2)

plot_financial_data(portfolio_df[['Date', 'Portfolio Daily Return [%]']], 'Portfolio Percentage Daily Return [%]')

# Plot each stock position in our portfolio over time
# This graph shows how our initial investment in each individual stock grows over time
plot_financial_data(portfolio_df.drop(['Portfolio Value [$]', 'Portfolio Daily Return [%]'], axis = 1), 'Portfolio positions [$]')

# Plot the total daily value of the portfolio (sum of all positions)
plot_financial_data(portfolio_df[['Date', 'Portfolio Value [$]']], 'Total Portfolio Value [$]')

# Let's define the simulation engine function 
# The function receives: 
    # (1) portfolio weights
    # (2) initial investment amount
# The function performs asset allocation and calculates portfolio statistical metrics including Sharpe ratio
# The function returns: 
    # (1) Expected portfolio return 
    # (2) Expected volatility 
    # (3) Sharpe ratio 
    # (4) Return on investment 
    # (5) Final portfolio value in dollars


def simulation_engine(weights, initial_investment):
    # Perform asset allocation using the random weights (sent as arguments to the function)
    portfolio_df = asset_allocation(close_price_df_subset, weights, initial_investment)
  
    # Calculate the return on the investment 
    # Return on investment is calculated using the last final value of the portfolio compared to its initial value
    return_on_investment = ((portfolio_df['Portfolio Value [$]'][-1:] - 
                             portfolio_df['Portfolio Value [$]'][0])/ 
                             portfolio_df['Portfolio Value [$]'][0]) * 100
  
    # Daily change of every stock in the portfolio (Note that we dropped the date, portfolio daily worth and daily % returns) 
    portfolio_daily_return_df = portfolio_df.drop(columns = ['Date', 'Portfolio Value [$]', 'Portfolio Daily Return [%]'])
    portfolio_daily_return_df = portfolio_daily_return_df.pct_change(1) 
  
    # Portfolio Expected Return formula
    expected_portfolio_return = np.sum(weights * portfolio_daily_return_df.mean() ) * 252
  
    # Portfolio volatility (risk) formula
    # The risk of an asset is measured using the standard deviation which indicates the dispertion away from the mean
    # The risk of a portfolio is not a simple sum of the risks of the individual assets within the portfolio
    # Portfolio risk must consider correlations between assets within the portfolio which is indicated by the covariance 
    # The covariance determines the relationship between the movements of two random variables
    # When two stocks move together, they have a positive covariance when they move inversely, the have a negative covariance 

    covariance = portfolio_daily_return_df.cov() * 252 
    expected_volatility = np.sqrt(np.dot(weights.T, np.dot(covariance, weights)))

    rf = 0.03 

    # Calculate Sharpe ratio
    sharpe_ratio = (expected_portfolio_return - rf)/expected_volatility 
    return expected_portfolio_return, expected_volatility, sharpe_ratio, portfolio_df['Portfolio Value [$]'][-1:].values[0], return_on_investment.values[0]

# Let's test out the "simulation_engine" function and print out statistical metrics
# Define the initial investment amount
initial_investment = 1000000
portfolio_metrics = simulation_engine(weights, initial_investment)

print('Expected Portfolio Annual Return = {:.2f}%'.format(portfolio_metrics[0] * 100))
print('Portfolio Standard Deviation (Volatility) = {:.2f}%'.format(portfolio_metrics[1] * 100))
print('Sharpe Ratio = {:.2f}'.format(portfolio_metrics[2]))
print('Portfolio Final Value = ${:.2f}'.format(portfolio_metrics[3]))
print('Return on Investment = {:.2f}%'.format(portfolio_metrics[4]))

Expected Portfolio Annual Return = 16.96%
Portfolio Standard Deviation (Volatility) = 20.08%
Sharpe Ratio = 0.69
Portfolio Final Value = $2591251.11
Return on Investment = 159.13%

# Set the number of simulation runs
sim_runs = 1000
initial_investment = 1000000

# Placeholder to store all weights
weights_runs = np.zeros((sim_runs, n))

# Placeholder to store all Sharpe ratios
sharpe_ratio_runs = np.zeros(sim_runs)

# Placeholder to store all expected returns
expected_portfolio_returns_runs = np.zeros(sim_runs)

# Placeholder to store all volatility values
volatility_runs = np.zeros(sim_runs)

# Placeholder to store all returns on investment
return_on_investment_runs = np.zeros(sim_runs)

# Placeholder to store all final portfolio values
final_value_runs = np.zeros(sim_runs)

for i in range(sim_runs):
    # Generate random weights 
    weights = generate_portfolio_weights(n)
    # Store the weights
    weights_runs[i,:] = weights
    
    # Call "simulation_engine" function and store Sharpe ratio, return and volatility
    # Note that asset allocation is performed using the "asset_allocation" function  
    expected_portfolio_returns_runs[i], volatility_runs[i], sharpe_ratio_runs[i], final_value_runs[i], return_on_investment_runs[i] = simulation_engine(weights, initial_investment)

# Return the index of the maximum Sharpe ratio (Best simulation run) 
sharpe_ratio_runs.argmax()

802

# Return the maximum Sharpe ratio value
sharpe_ratio_runs.max()

0.8069321105045569

#Obtain portfolio weights that correspond to maximum Sharpe Ratio
weights_runs[sharpe_ratio_runs.argmax(), :]

array([0.25925203, 0.0905036 , 0.09506045, 0.04489691, 0.07758603,
       0.01590731, 0.1133639 , 0.06804111, 0.01813227, 0.2172564 ])

# Return Sharpe ratio, volatility corresponding to the best weights allocation (maximum Sharpe ratio)
optimal_portfolio_return, optimal_volatility, optimal_sharpe_ratio, highest_final_value, optimal_return_on_investment = simulation_engine(weights_runs[sharpe_ratio_runs.argmax(), :], initial_investment)
print('Best Portfolio Metrics Based on {} Monte Carlo Simulation Runs:'.format(sim_runs))
print('  - Portfolio Expected Annual Return = {:.02f}%'.format(optimal_portfolio_return * 100))
print('  - Portfolio Standard Deviation (Volatility) = {:.02f}%'.format(optimal_volatility * 100))
print('  - Sharpe Ratio = {:.02f}'.format(optimal_sharpe_ratio))
print('  - Final Value = ${:.02f}'.format(highest_final_value))
print('  - Return on Investment = {:.02f}%'.format(optimal_return_on_investment))

Best Portfolio Metrics Based on 1000 Monte Carlo Simulation Runs:
  - Portfolio Expected Annual Return = 19.45%
  - Portfolio Standard Deviation (Volatility) = 20.38%
  - Sharpe Ratio = 0.81
  - Final Value = $2936480.02
  - Return on Investment = 193.65%

# Create a DataFrame that contains volatility, return, and Sharpe ratio for all simualation runs
sim_out_df = pd.DataFrame({'Volatility': volatility_runs.tolist(), 'Portfolio_Return': expected_portfolio_returns_runs.tolist(), 'Sharpe_Ratio': sharpe_ratio_runs.tolist() })
sim_out_df.head

<bound method NDFrame.head of      Volatility  Portfolio_Return  Sharpe_Ratio
0      0.186252          0.159995      0.697949
1      0.201230          0.169163      0.691564
2      0.190110          0.171342      0.743475
3      0.202554          0.179299      0.737084
4      0.195712          0.164027      0.684815
..          ...               ...           ...
995    0.190927          0.158218      0.671557
996    0.198990          0.171074      0.708951
997    0.199187          0.168732      0.696491
998    0.197026          0.162729      0.673664
999    0.195203          0.178939      0.762992

[1000 rows x 3 columns]>

# Plot volatility vs. return for all simulation runs
# Highlight the volatility and return that corresponds to the highest Sharpe ratio
import plotly.graph_objects as go
fig = px.scatter(sim_out_df, x = 'Volatility', y = 'Portfolio_Return', color = 'Sharpe_Ratio', size = 'Sharpe_Ratio', hover_data = ['Sharpe_Ratio'] )
fig.update_layout({'plot_bgcolor': "white"})
fig.show()

# Let's highlight the point with the highest Sharpe ratio
fig = px.scatter(sim_out_df, x = 'Volatility', y = 'Portfolio_Return', color = 'Sharpe_Ratio', size = 'Sharpe_Ratio', hover_data = ['Sharpe_Ratio'] )
fig.add_trace(go.Scatter(x = [optimal_volatility], y = [optimal_portfolio_return], mode = 'markers', name = 'Optimal Point', marker = dict(size=[40], color = 'red')))
fig.update_layout(coloraxis_colorbar = dict(y = 0.7, dtick = 5))
fig.update_layout({'plot_bgcolor': "white"})
fig.show()

# Plot interactive plot for volatility
fig = px.line(sim_out_df, y = 'Volatility')
fig.show()

# Plot interactive plot for Portfolio Return
fig = px.line(sim_out_df, y = 'Portfolio_Return')
fig.update_traces(line_color = 'red')
fig.show()

# Plot interactive plot for Portfolio Return
fig = px.line(sim_out_df, y = 'Sharpe_Ratio')
fig.update_traces(line_color = 'purple')
fig.show()

	Date	Open	High	Low	Close	Adj Close	Volume
0	2023-05-30	122.370003	122.919998	119.860001	121.660004	121.660004	64314800
1	2023-05-31	121.449997	122.040001	119.169998	120.580002	120.580002	72800800
2	2023-06-01	120.690002	123.489998	119.930000	122.769997	122.769997	54375100
3	2023-06-02	124.919998	126.389999	124.019997	124.250000	124.250000	61215000
4	2023-06-05	123.360001	125.800003	123.029999	125.300003	125.300003	47950100
5	2023-06-06	125.070000	127.400002	125.000000	126.610001	126.610001	45695200
6	2023-06-07	127.010002	127.370003	120.629997	121.230003	121.230003	95663300
7	2023-06-08	123.010002	125.629997	122.260002	124.250000	124.250000	62159300
8	2023-06-09	124.080002	125.800003	123.190002	123.430000	123.430000	51330000
9	2023-06-12	124.019997	126.779999	123.529999	126.570000	126.570000	51338000
10	2023-06-13	128.119995	128.410004	125.180000	126.660004	126.660004	50564800
11	2023-06-14	126.699997	126.949997	124.120003	126.419998	126.419998	52422500
12	2023-06-15	125.209999	127.690002	124.320000	127.110001	127.110001	60458500
13	2023-06-16	127.709999	127.900002	125.300003	125.489998	125.489998	84188100
14	2023-06-20	124.970001	127.250000	124.500000	125.779999	125.779999	56930100

	Date	Open	High	Low	Close	Adj Close	Volume	Daily Return
0	2023-05-30	122.370003	122.919998	119.860001	121.660004	121.660004	64314800	NaN
1	2023-05-31	121.449997	122.040001	119.169998	120.580002	120.580002	72800800	-0.887721
2	2023-06-01	120.690002	123.489998	119.930000	122.769997	122.769997	54375100	1.816217
3	2023-06-02	124.919998	126.389999	124.019997	124.250000	124.250000	61215000	1.205509
4	2023-06-05	123.360001	125.800003	123.029999	125.300003	125.300003	47950100	0.845073
...	...	...	...	...	...	...	...	...
245	2024-05-20	184.339996	186.669998	183.279999	183.539993	183.539993	30511800	-0.628048
246	2024-05-21	182.300003	183.259995	180.750000	183.149994	183.149994	50839100	-0.212487
247	2024-05-22	183.880005	185.220001	181.970001	183.130005	183.130005	28148800	-0.010914
248	2024-05-23	183.660004	184.759995	180.080002	181.050003	181.050003	33670200	-1.135806
249	2024-05-24	181.649994	182.440002	180.300003	180.750000	180.750000	27434100	-0.165702

	Date	Open	High	Low	Close	Adj Close	Volume	Daily Return
0	2023-05-30	122.370003	122.919998	119.860001	121.660004	121.660004	64314800	0.000000
1	2023-05-31	121.449997	122.040001	119.169998	120.580002	120.580002	72800800	-0.887721
2	2023-06-01	120.690002	123.489998	119.930000	122.769997	122.769997	54375100	1.816217
3	2023-06-02	124.919998	126.389999	124.019997	124.250000	124.250000	61215000	1.205509
4	2023-06-05	123.360001	125.800003	123.029999	125.300003	125.300003	47950100	0.845073
...	...	...	...	...	...	...	...	...
245	2024-05-20	184.339996	186.669998	183.279999	183.539993	183.539993	30511800	-0.628048
246	2024-05-21	182.300003	183.259995	180.750000	183.149994	183.149994	50839100	-0.212487
247	2024-05-22	183.880005	185.220001	181.970001	183.130005	183.130005	28148800	-0.010914
248	2024-05-23	183.660004	184.759995	180.080002	181.050003	181.050003	33670200	-1.135806
249	2024-05-24	181.649994	182.440002	180.300003	180.750000	180.750000	27434100	-0.165702

	Open	High	Low	Close	Adj Close	Volume	Daily Return
count	250.00	250.00	250.00	250.00	250.00	2.500000e+02	250.00
mean	150.63	152.22	149.02	150.68	150.68	4.924186e+07	0.17
std	21.17	21.22	21.04	21.14	21.14	1.789915e+07	1.77
min	120.63	121.64	118.35	119.57	119.57	2.237840e+07	-5.58
25%	131.41	133.02	129.62	131.73	131.73	3.891888e+07	-0.88
50%	145.11	147.06	144.17	145.52	145.52	4.574090e+07	0.05
75%	173.40	174.95	171.93	173.64	173.64	5.435895e+07	1.17
max	189.16	191.70	187.44	189.50	189.50	1.529387e+08	8.27

	Date	Open	High	Low	Close	Adj Close	Volume	Daily Return
0	2023-05-30	122.370003	122.919998	119.860001	121.660004	121.660004	64314800	0.000000
1	2023-05-31	121.449997	122.040001	119.169998	120.580002	120.580002	72800800	-0.887721
2	2023-06-01	120.690002	123.489998	119.930000	122.769997	122.769997	54375100	1.816217
3	2023-06-02	124.919998	126.389999	124.019997	124.250000	124.250000	61215000	1.205509
4	2023-06-05	123.360001	125.800003	123.029999	125.300003	125.300003	47950100	0.845073
...	...	...	...	...	...	...	...	...
245	2024-05-20	184.339996	186.669998	183.279999	183.539993	183.539993	30511800	-0.628048
246	2024-05-21	182.300003	183.259995	180.750000	183.149994	183.149994	50839100	-0.212487
247	2024-05-22	183.880005	185.220001	181.970001	183.130005	183.130005	28148800	-0.010914
248	2024-05-23	183.660004	184.759995	180.080002	181.050003	181.050003	33670200	-1.135806
249	2024-05-24	181.649994	182.440002	180.300003	180.750000	180.750000	27434100	-0.165702

FINAL CAPSTONE PROJECT OVERVIEW¶

Monte Carlo Simulation¶

	Date	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	...	Volume AMZN	Volume CAT	Volume DE	Volume EXC	Volume GOOGL	Volume JNJ	Volume JPM	Volume META	Volume PFE	Volume PG
0	02/01/2018	59.450500	133.842728	142.165344	22.141459	53.466679	115.682427	88.566345	180.729614	25.982754	...	53890000	5108400	1897600	7224506	31766000	6842100	13578800	18151900	17059833	7558900
1	03/01/2018	60.209999	134.047287	142.813110	21.845049	54.378876	116.787498	88.656593	183.967255	26.175268	...	62176000	4146100	1392300	6750350	31318000	5350500	11901000	16886600	14183151	5863600
2	04/01/2018	60.479500	135.888229	143.631912	21.537451	54.590103	116.779190	89.926659	183.628555	26.232313	...	60442000	4865800	1747000	6630619	26052000	4837600	12953700	13880900	13046517	6322500
3	05/01/2018	61.457001	138.035919	144.243759	21.358482	55.313984	117.743027	89.349350	186.138962	26.282223	...	70894000	3945400	1574400	10013224	30250000	6158800	14155000	13574500	13167517	5949700
4	08/01/2018	62.343498	141.504761	144.954468	21.587782	55.509274	117.892593	89.481308	187.563522	25.989882	...	85590000	5929200	2711800	15116925	24644000	5129800	12466500	17994700	19964341	4860700
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1759	30/12/2024	221.300003	363.010010	423.020019	37.630001	191.240005	143.339996	239.320007	591.239990	26.420000	...	28321200	1422200	792500	4145100	14264700	6268700	5723800	7025900	32859100	4354500
1760	31/12/2024	219.389999	362.760010	423.700012	37.639999	189.300003	144.619995	239.710007	585.510010	26.530001	...	24819700	1168100	772700	5347900	17466900	5811400	4871000	6019500	27085500	3957600
1761	02/01/2025	220.220001	359.769989	418.179993	37.660000	189.429993	144.020004	240.000000	599.239990	26.610001	...	33956600	1802600	1005300	4578400	20370800	6051300	9220900	12682300	32899200	5401700
1762	03/01/2025	224.190002	363.790008	422.220001	38.049999	191.789993	144.190002	243.279999	604.630005	26.590000	...	27485000	1390800	875100	4268100	18582000	5878800	9460700	11418600	33403100	5256600
1763	06/01/2025	228.149994	366.559998	423.644989	37.735001	197.110001	143.679993	242.264999	617.349976	26.850000	...	11201274	413486	201880	886842	9118320	1716732	2527399	3611517	9345052	1788640

	Date	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	...	Volume AMZN	Volume CAT	Volume DE	Volume EXC	Volume GOOGL	Volume JNJ	Volume JPM	Volume META	Volume PFE	Volume PG
0	02/01/2018	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	03/01/2018	1.012775	1.001528	1.004556	0.986613	1.017061	1.009553	1.001019	1.017914	1.007409	...	1.153758	0.811624	0.733716	0.934368	0.985897	0.781997	0.876440	0.930294	0.831377	0.775721
2	04/01/2018	1.017309	1.015283	1.010316	0.972721	1.021012	1.009481	1.015359	1.016040	1.009605	...	1.121581	0.952510	0.920637	0.917795	0.820122	0.707034	0.953965	0.764708	0.764751	0.836431
3	05/01/2018	1.033751	1.031329	1.014620	0.964638	1.034551	1.017813	1.008841	1.029931	1.011526	...	1.315532	0.772336	0.829680	1.386008	0.952276	0.900133	1.042434	0.747828	0.771843	0.787112
4	08/01/2018	1.048662	1.057247	1.019619	0.974994	1.038203	1.019105	1.010331	1.037813	1.000274	...	1.588235	1.160677	1.429068	2.092451	0.775798	0.749741	0.918086	0.991340	1.170254	0.643043
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1759	30/12/2024	3.722425	2.712213	2.975550	1.699527	3.576807	1.239082	2.702155	3.271406	1.016828	...	0.525537	0.278404	0.417633	0.573755	0.449056	0.916195	0.421525	0.387061	1.926109	0.576076
1760	31/12/2024	3.690297	2.710345	2.980333	1.699978	3.540523	1.250147	2.706559	3.239702	1.021062	...	0.460562	0.228663	0.407199	0.740244	0.549861	0.849359	0.358721	0.331618	1.587677	0.523568
1761	02/01/2025	3.704258	2.688006	2.941504	1.700882	3.542954	1.244960	2.709833	3.315671	1.024141	...	0.630109	0.352870	0.529774	0.633732	0.641277	0.884421	0.679066	0.698676	1.928460	0.714615
1762	03/01/2025	3.771036	2.718041	2.969922	1.718496	3.587094	1.246430	2.746867	3.345495	1.023371	...	0.510020	0.272257	0.461161	0.590781	0.584965	0.859210	0.696726	0.629058	1.957997	0.695419
1763	06/01/2025	3.837646	2.738737	2.979946	1.704269	3.686595	1.242021	2.735407	3.415876	1.033378	...	0.207854	0.080942	0.106387	0.122755	0.287047	0.250907	0.186128	0.198961	0.547781	0.236627

	Date	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	Adj Close PG
0	02/01/2018	122835.6	110859.4	70782.8	1708.8	135106.5	137036.6	133277.1	125820.3	82182.4	80390.4
1	03/01/2018	124404.9	111028.8	71105.4	1686.0	137411.6	138345.7	133413.0	128074.3	82791.3	80292.8
2	04/01/2018	124961.7	112553.7	71513.0	1662.2	137945.3	138335.8	135324.2	127838.5	82971.7	80860.4
3	05/01/2018	126981.4	114332.6	71817.7	1648.4	139774.5	139477.6	134455.4	129586.2	83129.6	80913.6
4	08/01/2018	128813.1	117205.7	72171.5	1666.1	140268.0	139654.7	134654.0	130577.9	82204.9	81339.3
...	...	...	...	...	...	...	...	...	...	...	...
1759	30/12/2024	457246.3	300674.4	210617.8	2904.2	483250.0	169799.6	360135.5	411609.3	83565.4	178433.4
1760	31/12/2024	453299.9	300467.3	210956.4	2905.0	478347.7	171315.8	360722.4	407620.2	83913.3	179031.5
1761	02/01/2025	455014.8	297990.7	208208.0	2906.5	478676.2	170605.1	361158.8	417178.7	84166.3	177248.1
1762	03/01/2025	463217.6	301320.4	210219.5	2936.6	484639.8	170806.5	366094.6	420931.2	84103.1	176340.4
1763	06/01/2025	471399.7	303614.7	210929.0	2912.3	498083.0	170202.3	364567.2	429786.5	84925.4	172752.3

	Date	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	Adj Close PG	Portfolio Value [$]	Portfolio Daily Return [%]
0	02/01/2018	86754.00	24816.00	122970.00	85382.00	128072.00	123358.00	165131.00	131114.00	117482.00	14920.00	999999.00	0.00
1	03/01/2018	87862.31	24853.93	123530.30	84238.98	130257.04	124536.39	165299.27	133462.81	118352.46	14901.89	1007295.39	0.73
2	04/01/2018	88255.58	25195.26	124238.55	83052.82	130763.01	124527.53	167667.29	133217.10	118610.39	15007.23	1010534.77	0.32
3	05/01/2018	89682.01	25593.47	124767.78	82362.68	132496.96	125555.32	166590.90	135038.32	118836.06	15017.11	1015940.63	0.53
4	08/01/2018	90975.65	26236.63	125382.53	83246.91	132964.75	125714.81	166836.94	136071.80	117514.23	15096.11	1020040.36	0.40
...	...	...	...	...	...	...	...	...	...	...	...	...	...
1759	30/12/2024	322935.22	67306.28	365903.32	145108.99	458088.86	152850.66	446209.58	428927.16	119459.03	33116.24	2539905.35	-0.95
1760	31/12/2024	320148.02	67259.93	366491.50	145147.55	453441.86	154215.59	446936.73	424770.23	119956.40	33227.23	2531595.04	-0.33
1761	02/01/2025	321359.21	66705.54	361716.80	145224.67	453753.23	153575.79	447477.42	434730.92	120318.12	32896.25	2537757.96	0.24
1762	03/01/2025	327152.49	67450.90	365211.32	146728.59	459406.28	153757.06	453592.95	438641.22	120227.69	32727.79	2564896.30	1.07
1763	06/01/2025	332931.17	67964.49	366443.91	145513.89	472149.62	153213.22	451700.49	447869.18	121403.29	32061.85	2591251.11	1.03

	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	Adj Close PG
0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1	1.277531	0.152836	0.455643	-1.338709	1.706104	0.955264	0.101899	1.791428	0.740932	-0.121353
2	0.447601	1.373353	0.573338	-1.408091	0.388437	-0.007114	1.432567	-0.184108	0.217935	0.706883
3	1.616252	1.580483	0.425983	-0.830964	1.326029	0.825350	-0.641977	1.367111	0.190260	0.065795
4	1.442468	2.512999	0.492714	1.073576	0.353056	0.127028	0.147688	0.765321	-1.112315	0.526085
...	...	...	...	...	...	...	...	...	...	...
1759	-1.094971	-0.507037	-1.260282	0.534333	-0.788540	-1.178908	-0.767090	-1.428787	-0.751318	-1.439275
1760	-0.863083	-0.068869	0.160747	0.026570	-1.014433	0.892981	0.162961	-0.969146	0.416354	0.335147
1761	0.378323	-0.824242	-1.302813	0.053136	0.068669	-0.414874	0.120977	2.344961	0.301545	-0.996122
1762	1.802743	1.117386	0.966093	1.035580	1.245843	0.118038	1.366666	0.899475	-0.075161	-0.512104
1763	1.766355	0.761425	0.337499	-0.827854	2.773871	-0.353707	-0.417214	2.103761	0.977812	-2.034761

	Date	Adj Close AMZN	Adj Close CAT	Adj Close DE	Adj Close EXC	Adj Close GOOGL	Adj Close JNJ	Adj Close JPM	Adj Close META	Adj Close PFE	Adj Close PG
0	02/01/2018	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1	03/01/2018	1.277531	0.152836	0.455643	-1.338709	1.706104	0.955264	0.101899	1.791428	0.740932	-0.121353
2	04/01/2018	0.447601	1.373353	0.573338	-1.408091	0.388437	-0.007114	1.432567	-0.184108	0.217935	0.706883
3	05/01/2018	1.616252	1.580483	0.425983	-0.830964	1.326029	0.825350	-0.641977	1.367111	0.190260	0.065795
4	08/01/2018	1.442468	2.512999	0.492714	1.073576	0.353056	0.127028	0.147688	0.765321	-1.112315	0.526085
...	...	...	...	...	...	...	...	...	...	...	...
1759	30/12/2024	-1.094971	-0.507037	-1.260282	0.534333	-0.788540	-1.178908	-0.767090	-1.428787	-0.751318	-1.439275
1760	31/12/2024	-0.863083	-0.068869	0.160747	0.026570	-1.014433	0.892981	0.162961	-0.969146	0.416354	0.335147
1761	02/01/2025	0.378323	-0.824242	-1.302813	0.053136	0.068669	-0.414874	0.120977	2.344961	0.301545	-0.996122
1762	03/01/2025	1.802743	1.117386	0.966093	1.035580	1.245843	0.118038	1.366666	0.899475	-0.075161	-0.512104
1763	06/01/2025	1.766355	0.761425	0.337499	-0.827854	2.773871	-0.353707	-0.417214	2.103761	0.977812	-2.034761